How We Detect Anomalies Without Looking at Graphs

A deep dive into our anomaly detection pipeline: statistical methods, ML models, and how we correlate anomalies to root causes automatically.

The Traditional Approach

Most anomaly detection still works like this: 1. Human sets thresholds 2. Alert fires when threshold crossed 3. Human investigates 4. Human finds root cause

This is slow, error-prone, and doesn't scale.

Our Approach: Automated Correlation

FluxPoint's anomaly detection works differently:

Layer 1: Statistical Detection - Adaptive thresholds - Learns normal behavior per metric - Seasonality handling - Accounts for daily/weekly patterns - Multi-metric correlation - Detects anomalies in related metrics

Layer 2: Log Pattern Analysis - Streaming pattern detection - Finds unusual log sequences - Error rate correlation - Links error spikes to anomalies - Service dependency awareness - Knows how services relate

Layer 3: ML-Based Prioritization - Severity scoring - Predicts business impact - False positive reduction - Learns from operator feedback - Causal inference - Distinguishes correlation from causation

The Correlation Engine

The magic is in automatic correlation. When an anomaly is detected:

**Trace linking** - Correlates to specific requests/operations
**Log correlation** - Finds related log entries
**Code context** - Links to relevant code changes
**Historical patterns** - Compares to similar past incidents

Results

**90%+ accuracy** on critical anomaly detection
**80% reduction** in false positives vs threshold-based alerting
**Median 30 seconds** from anomaly occurrence to agent-consumable context

For AI Agents

All of this produces structured, agent-readable output: - Anomaly context with severity and confidence - Correlated signals and evidence - Suggested investigation paths - Related historical incidents

Agents can investigate without human interpretation.